DATA STRUCTURES you MUST know (as a Software Developer)

without data structures you can't solve algorithms and without algorithms you can't pass a programming interview now the problem is if you open a data structures and algorithms text book it is really mathy and if you're like me and you got a C in eighth grade math it's gonna scare you a lot the good news is once you understand data structures they're not actually that complicated so I wanted to explain the five most important data structures in this video in the way I understand them which means as little technical jargon as possible and just try to simplify it for you guys just starting out let's go first off let's talk about what a data structure is consider having a single data item like the price of a stock now that price on its own is not going to mean much but if we put it together with a bunch of stock prices let's say the price is over an entire day well then it becomes more meaningful we call date items that we grouped together like this compound data we have to store this data in some kind of a data structure and choosing the right one is really important no data structure is perfect all them are good and bad at different things the way we measure how good a data structure is doing a specific thing like adding a new item retrieving an item sorting or searching is called the Big O notation this is pretty much a measure of how well in operation scales so if you have ten items and then you add a million more how much longer will each operation take okay let's talk about the first data structure which is the linked list the atomic unit of the linked list is something called a node which contains a value and a pointer the value is something as simple as a number like 12 and that pointer will simply connect you to the next node in the chain hence the linked part of linked lists the first node in the list is known as the head while the last one that doesn't have a next pointer is known as the tail pros and cons of the linked list it's really good at adding new nodes and deleting nodes because we can just change where the next pointer is pointing however it's not too good at retrieving notes even when we know that index or searching because each node is only aware of the node next to it data structure number two is going to be the array you might already be familiar with arrays because they're super common in all programming languages the array is quite literally a continuous block of cells in the computer memory by keeping track of its memory location let's say the location is 1000 it can instantly compute the location of any item inside of it for example let's say we want to get index number 5 we just add 5 to a thousand get a thousand and 5 then we can pull that value directly out as a result of this you can probably imagine arrays are really good at retrieving items however consider this your array keeps growing in size and you start running into other things in memory for this reason adding isn't always too efficient because we might have to move our array to a new place so it fits luckily this happens under the hood in high-level languages like JavaScript and Python but in lower-level languages you have to declare the size of your array in advance okay the third data structure and this one is super important is known as the hash table you might be familiar with it because it's an object in JavaScript or a dict or dictionary in Python I think dictionary is a great word because you basically give the hash table word or a key and it will retrieve the definition or the value for you under the hood it actually works a lot like an array the key actually gets run through a function called the hashing function and that will spit out a memory location for you the way it's different is these memory locations don't have to be next to each other they can be anywhere so you don't have the same problem with the increasing size however there is a different problem depending on the hashing algorithm you use two keys could hash to the same memory location this is what's known as the collision and there are different ways to resolve this but again it's all happening under the hood needless to say the hash tables really good at both retrieving and adding but the collisions can be a problem okay fourth data structure this is a double is the stack and the queue I'm grouping them together because they're pretty similar and they're both kind of built on top of arrays with a few additional features the stack is a blast in first out data structure think of a stack of cafeteria trays the last one you put on the top is the first one you have to take off when we add an item to the top it's called pushing and then we pop off the top every language keeps track of the functions that have been called with something called the call stack stacks are also super important for an algorithm called depth-first search which you're gonna run into a lot the queue on the other hand is first in first add just like any hue or line let's say a line to board a plane at the airport adding an item to the end is called in queueing and removing it from the front is D queuing queues are used for a really important algorithm called breadth-first search over all stacks and queues are really efficient but have limited use cases compared to other data structures finally we're gonna talk about graphs and trees this one is so big if an entire field of computer science called graph theory what a graph is is basically kind of similar to a linked list where you have nodes that are pointing to other nodes except in this case the pointers are called edges they just could also have weights or numbers assigned to them imagine two cities New York and Boston the road between them is the edge and the length of the road can be the weight of that edge complicated relationships like social media networks are also stored as graphs there's a special kind of a hierarchical graph called a tree in which the data expands out in one direction we can use these to represent a lot of things - like a family tree or even an HTML tree with nested elements there's an even more specific tree called a binary search tree this tree has really specific rules but these rules allow us to do things like searching really really efficiently the rules are as follows each node can only have maximum two children left and right the left has to be less than the node and the right has to be more with these rules in place we can traverse through our tree and always kind of know where an element is so if I had a five at the top and I was looking for seven I would know it's either to the right or not in my tree at all unfortunately BST is not the perfect data structure either if you add elements in a weird order it can get very unbalanced or one-sided and you lose a lot of the advantages you get with your search optimization there are self balancing trees but that's getting into more advanced data structures so we'll leave those out of here all right guys these five data structures will give you a really great foundation to start solving algorithms this video is not sponsored but I would recommend checking out code Wars first followed by leet code when you get a little bit better Lee code has programming interview questions from actual tech companies so it's really good to eventually get there anyway guys I hope you liked this video and if you could leave it a like it's free for you so that would really help me out and if you want to see more short no BS tutorials then please subscribe alright I will catch you guys soon [Music]

Channel: Aaron Jack

Views: 534,205

Rating: 4.9586658 out of 5

Keywords: web development, learn programming, become a software developer, data structures, learn data structures, programming data structures, coding data structures, cs data structures, computer science data structures, data structures fundamentals, data structures and algorithms, basic data structures, data structures overview, data structures explained, computer science basics, computer science fundamentals, learn computer science, linked list, binary search tree, hash table

Id: sVxBVvlnJsM

Length: 7min 23sec (443 seconds)

Published: Mon Dec 23 2019